bool RandomForests::train(LabelledClassificationData trainingData){
//Clear any previous model
clear();
const unsigned int M = trainingData.getNumSamples();
const unsigned int N = trainingData.getNumDimensions();
const unsigned int K = trainingData.getNumClasses();
if( M == 0 ){
errorLog << "train(LabelledClassificationData labelledTrainingData) - Training data has zero samples!" << endl;
return false;
}
numInputDimensions = N;
numClasses = K;
classLabels = trainingData.getClassLabels();
ranges = trainingData.getRanges();
//Scale the training data if needed
if( useScaling ){
//Scale the training data between 0 and 1
trainingData.scale(0, 1);
}
//Train the random forest
forestSize = 10;
Random random;
DecisionTree tree;
tree.enableScaling( false ); //We have already scaled the training data so we do not need to scale it again
tree.setTrainingMode( DecisionTree::BEST_RANDOM_SPLIT );
tree.setNumSplittingSteps( numRandomSplits );
tree.setMinNumSamplesPerNode( minNumSamplesPerNode );
tree.setMaxDepth( maxDepth );
for(UINT i=0; i<forestSize; i++){
LabelledClassificationData data = trainingData.getBootstrappedDataset();
if( !tree.train( data ) ){
errorLog << "train(LabelledClassificationData labelledTrainingData) - Failed to train tree at forest index: " << i << endl;
return false;
}
//Deep copy the tree into the forest
forest.push_back( tree.deepCopyTree() );
}
//Flag that the algorithm has been trained
trained = true;
return trained;
}
bool RandomForests::train_(ClassificationData &trainingData){
//Clear any previous model
clear();
const unsigned int M = trainingData.getNumSamples();
const unsigned int N = trainingData.getNumDimensions();
const unsigned int K = trainingData.getNumClasses();
if( M == 0 ){
errorLog << "train_(ClassificationData &trainingData) - Training data has zero samples!" << endl;
return false;
}
if( bootstrappedDatasetWeight <= 0.0 || bootstrappedDatasetWeight > 1.0 ){
errorLog << "train_(ClassificationData &trainingData) - Bootstrapped Dataset Weight must be [> 0.0 and <= 1.0]" << endl;
return false;
}
numInputDimensions = N;
numClasses = K;
classLabels = trainingData.getClassLabels();
ranges = trainingData.getRanges();
//Scale the training data if needed
if( useScaling ){
//Scale the training data between 0 and 1
trainingData.scale(0, 1);
}
//Flag that the main algorithm has been trained encase we need to trigger any callbacks
trained = true;
//Train the random forest
forest.reserve( forestSize );
for(UINT i=0; i<forestSize; i++){
//Get a balanced bootstrapped dataset
UINT datasetSize = (UINT)(trainingData.getNumSamples() * bootstrappedDatasetWeight);
ClassificationData data = trainingData.getBootstrappedDataset( datasetSize, true );
DecisionTree tree;
tree.setDecisionTreeNode( *decisionTreeNode );
tree.enableScaling( false ); //We have already scaled the training data so we do not need to scale it again
tree.setTrainingMode( trainingMode );
tree.setNumSplittingSteps( numRandomSplits );
tree.setMinNumSamplesPerNode( minNumSamplesPerNode );
tree.setMaxDepth( maxDepth );
tree.enableNullRejection( useNullRejection );
tree.setRemoveFeaturesAtEachSpilt( removeFeaturesAtEachSpilt );
trainingLog << "Training forest " << i+1 << "/" << forestSize << "..." << endl;
//Train this tree
if( !tree.train( data ) ){
errorLog << "train_(ClassificationData &labelledTrainingData) - Failed to train tree at forest index: " << i << endl;
clear();
return false;
}
//Deep copy the tree into the forest
forest.push_back( tree.deepCopyTree() );
}
return true;
}
bool RandomForests::train_(ClassificationData &trainingData){
//Clear any previous model
clear();
const unsigned int M = trainingData.getNumSamples();
const unsigned int N = trainingData.getNumDimensions();
const unsigned int K = trainingData.getNumClasses();
if( M == 0 ){
errorLog << "train_(ClassificationData &trainingData) - Training data has zero samples!" << std::endl;
return false;
}
if( bootstrappedDatasetWeight <= 0.0 || bootstrappedDatasetWeight > 1.0 ){
errorLog << "train_(ClassificationData &trainingData) - Bootstrapped Dataset Weight must be [> 0.0 and <= 1.0]" << std::endl;
return false;
}
numInputDimensions = N;
numClasses = K;
classLabels = trainingData.getClassLabels();
ranges = trainingData.getRanges();
//Scale the training data if needed
if( useScaling ){
//Scale the training data between 0 and 1
trainingData.scale(0, 1);
}
if( useValidationSet ){
validationSetAccuracy = 0;
validationSetPrecision.resize( useNullRejection ? K+1 : K, 0 );
validationSetRecall.resize( useNullRejection ? K+1 : K, 0 );
}
//Flag that the main algorithm has been trained encase we need to trigger any callbacks
trained = true;
//Train the random forest
forest.reserve( forestSize );
for(UINT i=0; i<forestSize; i++){
//Get a balanced bootstrapped dataset
UINT datasetSize = (UINT)(trainingData.getNumSamples() * bootstrappedDatasetWeight);
ClassificationData data = trainingData.getBootstrappedDataset( datasetSize, true );
Timer timer;
timer.start();
DecisionTree tree;
tree.setDecisionTreeNode( *decisionTreeNode );
tree.enableScaling( false ); //We have already scaled the training data so we do not need to scale it again
tree.setUseValidationSet( useValidationSet );
tree.setValidationSetSize( validationSetSize );
tree.setTrainingMode( trainingMode );
tree.setNumSplittingSteps( numRandomSplits );
tree.setMinNumSamplesPerNode( minNumSamplesPerNode );
tree.setMaxDepth( maxDepth );
tree.enableNullRejection( useNullRejection );
tree.setRemoveFeaturesAtEachSpilt( removeFeaturesAtEachSpilt );
trainingLog << "Training decision tree " << i+1 << "/" << forestSize << "..." << std::endl;
//Train this tree
if( !tree.train_( data ) ){
errorLog << "train_(ClassificationData &trainingData) - Failed to train tree at forest index: " << i << std::endl;
clear();
return false;
}
Float computeTime = timer.getMilliSeconds();
trainingLog << "Decision tree trained in " << (computeTime*0.001)/60.0 << " minutes" << std::endl;
if( useValidationSet ){
Float forestNorm = 1.0 / forestSize;
validationSetAccuracy += tree.getValidationSetAccuracy();
VectorFloat precision = tree.getValidationSetPrecision();
VectorFloat recall = tree.getValidationSetRecall();
grt_assert( precision.getSize() == validationSetPrecision.getSize() );
grt_assert( recall.getSize() == validationSetRecall.getSize() );
for(UINT i=0; i<validationSetPrecision.getSize(); i++){
validationSetPrecision[i] += precision[i] * forestNorm;
}
for(UINT i=0; i<validationSetRecall.getSize(); i++){
validationSetRecall[i] += recall[i] * forestNorm;
}
}
//Deep copy the tree into the forest
forest.push_back( tree.deepCopyTree() );
}
if( useValidationSet ){
validationSetAccuracy /= forestSize;
trainingLog << "Validation set accuracy: " << validationSetAccuracy << std::endl;
trainingLog << "Validation set precision: ";
for(UINT i=0; i<validationSetPrecision.getSize(); i++){
trainingLog << validationSetPrecision[i] << " ";
}
trainingLog << std::endl;
trainingLog << "Validation set recall: ";
for(UINT i=0; i<validationSetRecall.getSize(); i++){
trainingLog << validationSetRecall[i] << " ";
}
trainingLog << std::endl;
}
return true;
}
int main(int argc, const char * argv[])
{
//Parse the data filename from the argument list
if( argc != 2 ){
cout << "Error: failed to parse data filename from command line. You should run this example with one argument pointing to the data filename!\n";
return EXIT_FAILURE;
}
const string filename = argv[1];
//Create a new DecisionTree instance
DecisionTree dTree;
//Set the node that the DecisionTree will use - different nodes may result in different decision boundaries
//and some nodes may provide better accuracy than others on specific classification tasks
//The current node options are:
//- DecisionTreeClusterNode
//- DecisionTreeThresholdNode
dTree.setDecisionTreeNode( DecisionTreeClusterNode() );
//Set the number of steps that will be used to choose the best splitting values
//More steps will give you a better model, but will take longer to train
dTree.setNumSplittingSteps( 1000 );
//Set the maximum depth of the tree
dTree.setMaxDepth( 10 );
//Set the minimum number of samples allowed per node
dTree.setMinNumSamplesPerNode( 10 );
//Load some training data to train the classifier
ClassificationData trainingData;
if( !trainingData.load( filename ) ){
cout << "Failed to load training data: " << filename << endl;
return EXIT_FAILURE;
}
//Use 20% of the training dataset to create a test dataset
ClassificationData testData = trainingData.split( 80 );
//Train the classifier
if( !dTree.train( trainingData ) ){
cout << "Failed to train classifier!\n";
return EXIT_FAILURE;
}
//Print the tree
dTree.print();
//Save the model to a file
if( !dTree.save("DecisionTreeModel.grt") ){
cout << "Failed to save the classifier model!\n";
return EXIT_FAILURE;
}
//Load the model from a file
if( !dTree.load("DecisionTreeModel.grt") ){
cout << "Failed to load the classifier model!\n";
return EXIT_FAILURE;
}
//Test the accuracy of the model on the test data
double accuracy = 0;
for(UINT i=0; i<testData.getNumSamples(); i++){
//Get the i'th test sample
UINT classLabel = testData[i].getClassLabel();
VectorDouble inputVector = testData[i].getSample();
//Perform a prediction using the classifier
bool predictSuccess = dTree.predict( inputVector );
if( !predictSuccess ){
cout << "Failed to perform prediction for test sampel: " << i <<"\n";
return EXIT_FAILURE;
}
//Get the predicted class label
UINT predictedClassLabel = dTree.getPredictedClassLabel();
VectorDouble classLikelihoods = dTree.getClassLikelihoods();
VectorDouble classDistances = dTree.getClassDistances();
//Update the accuracy
if( classLabel == predictedClassLabel ) accuracy++;
cout << "TestSample: " << i << " ClassLabel: " << classLabel << " PredictedClassLabel: " << predictedClassLabel << endl;
}
cout << "Test Accuracy: " << accuracy/double(testData.getNumSamples())*100.0 << "%" << endl;
return EXIT_SUCCESS;
}